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2-1
l Chapter 2 l
Statistical Concepts and Language
2.1 The Difference Between the
Population and a Sample
2.2 The Difference Between the
Parameter and a Statistics
2.3 Measurement Levels
2.4 Sampling Methods
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-2
2.0 Statistical Concepts and Language
 Data

Set:
Measurements of items


e.g., Yearly sales volume for your 23 salespeople
e.g., Cost and number produced, daily, for the past month
 Elementary

The items being measured

A

Units:
e.g., Salespeople, Days, Companies, Catalogs, …
Variable:
The type of measurement being done

e.g., Sales volume, Cost, Productivity, Number of defects, …
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-3
2.0 Statistical Concepts and Language
How Many Variables?
 Univariate
data set: One variable measured for each
elementary unit


e.g., Sales for the top 30 computer companies.
Can do: Typical summary, diversity, special features
 Bivariate


data set: Two variables
e.g., Sales and # Employees for top 30 computer firms
Can also do: relationship, prediction
 Multivariate


data set: Three or more variables
e.g., Sales, # Employees, Inventories, Profits, …
Can also do: predict one from all other variables
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-4
2.1 The Difference Between the Population
and a Sample
 Population

Consist of all the items or individuals about which you want to reach
conclusions
 Sample

The portion of a population selected for analysis
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-5
2.2 The Difference Between the Parameter
and a Statistics
 Population

A measure that describes a characteristics of a population
 Sample

parameter
statistics
A measure that describes a characteristics of a sample
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-6
2.3 Measurement Levels
 Qualitative


Variable: Categories
Nominal Variable: categories without meaningful ordering
 e.g., State, Type of business, Field of study
 Can count
Ordinal Variable: Categories with meaningful ordering
 e.g., The ranking of favorite sports, the order of people's
place in a line, the order of runners finishing a race

Can rank, count
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-7
2.3 Measurement Levels
 Quantitative


Variable: Interval and Ratio
Interval Variable: like ordinal except we can say the intervals
between each value are equally split
 e.g., temperature
 Can add, rank, count, without true zero
Ratio Variable: interval data with a natural zero point

e.g., Time and weight

Can add, rank, count, with true zero
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-8
2.4 Sampling Methods
 Type

of Sampling Method
Probability Sampling
 Simple Random Sampling
 Stratified Sampling
Cluster Sampling
 Systematic Sampling


Nonprobability Sampling
 Convenience Sampling
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-9
2.4 Sampling Methods
 Probability

Sampling
Simple Random Sampling
 every item from a frame has the same chance of selection as
every other item.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-10
2.4 Sampling Methods
 Probability

Sampling
Stratified Sampling
 Subdivide the N items in the frame into separate subpopulations
(strata). A stratum is defined by some common characteristic,
e.g.: gender or year in school. Conduct simple random
sampling within each strata and combine the results
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-11
2.4 Sampling Methods
 Probability

Sampling
Cluster Sampling
 Divide the N items in the frame into
clusters that contain several items.
Clusters are often naturally occurring
designations, such as counties, election
districts, city blocks, households, or
sales territories. Then take a random
sample of one or more clusters and
study all items in each selected cluster.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-12
2.4 Sampling Methods
 Probability

Sampling
Systematic Sampling
 Partitioned the N items in the frame into n groups of k items,
where
N
k
n
and round k to the nearest
integer.
 Then choose the first
item to be selected
at random from the first k items in the frame. Then, select the
remaining items by taking every kth item thereafter.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
2-13
2.4 Sampling Methods
 Nonprobability

Sampling
Convenience/Accidental Sampling
 Items selected are easy, inexpensive, or convenient to sample.
For example, if you were sampling tires stacked in a
warehouse, it would be much more convenient to sample tires
at the top of a stack than tires at the bottom of a stack.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000